Circular Back-Propagation Networks for Measuring Displayed Image Quality

Abstract

A system based on a neural-network estimates the perceived quality of digital pictures that had previously undergone image-enhancement algorithms. The objective system exploits the ability of feed-forward networks to handle multidimensional data with non-linear relationships. A Circular Back-Propagation network maps feature vectors into the associated quality ratings, thus estimating perceived quality. Feature vectors characterize the image at a global level by exploiting statistical properties of objective features, which are extracted on a block-by-block basis. A feature-selection procedure based on statistical analysis drives the composition of the objective metric set. Experimental results confirm the approach effectiveness, as the system provides a satisfactory approximation of subjective tests involving human voters.